CVE-2024-5206

Sensitive-data exposure via retained discarded tokens

Published Jun 6, 2024 · Updated Aug 1, 2024

Sensitive-data exposure in scikit-learn before 1.5.0 allows local users to read discarded training tokens from fitted vectorizers. TfidfVectorizer and CountVectorizer place tokens excluded by min_df, max_df, or max_features into the inspection-only stop_words_ attribute, which persists with a fitted model. Exposure requires low-privileged access to a fitted vectorizer or its serialized state; consequences are limited to disclosure of tokens present in its training data.

CVSS severity4.7
Medium
EPSS probability0.19%
Next 30 days · Sep 16, 2026
Known exploitationUnconfirmed
Based on sourced intelligence
Hinoki checkNot available
Coverage for this vulnerability

See if you're affected

Explore vulnerability checks for your environment with Hinoki.

Book a demo

Summary

What happened

Sensitive-data exposure in scikit-learn before 1.5.0 allows local users to read discarded training tokens from fitted vectorizers. TfidfVectorizer and CountVectorizer place tokens excluded by min_df, max_df, or max_features into the inspection-only stop_words_ attribute, which persists with a fitted model. Exposure requires low-privileged access to a fitted vectorizer or its serialized state; consequences are limited to disclosure of tokens present in its training data.

The record

CVE
CVE-2024-5206
Published
Jun 6, 2024
Updated
Aug 1, 2024
Vendor
scikit-learn
Product
scikit-learn
Classifications
CWE-921, CWE-922, T1005
Attack vector
local
Privileges
authenticated

Timeline

How it unfolded

  1. Jun 6, 2024CVE publishedPublication date reported by the CVE source.
  2. Aug 1, 2024Record updatedLatest update available in the CVE record.

Exploitability

Present is not the same as exploitable

Compare your product and version with the public record. A matching version still requires validation against your environment.

Is a vulnerable build present?

Compare these published version ranges with your installed build and any vendor patches.

  1. Affected versionversion=0 <1.5.0
  2. Affected versionversion=unspecified <1.5.0

What conditions does exploitation require?

Attack vectorlocal
Required privilegesauthenticated

What is affected?

scikit-learn · scikit-learnversion=0 <1.5.0; version=unspecified <1.5.0

Published CVSS scores

4.7NVDCVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:N/A:N
5.3GitHubCVSS:3.0/AV:N/AC:H/PR:L/UI:N/S:U/C:H/I:N/A:N
4.7Protect AICVSS:3.0/AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:N/A:N

CVSS describes severity. EPSS estimates exploitation probability.

Attacks

What attackers are doing with it

Daily unique IPs observed by Shadowserver honeypots for known exploited vulnerabilities (KEVs). Missing observations do not establish an absence of attacks.

Daily unique IPsNo honeypot observations are available for this CVE in the selected window.

No observations available

Sep 9, 2026Sep 15, 2026
Latest reporting daySep 15, 2026
Latest daily unique IPsUnavailable
Prior 30-day averageUnavailable
SourceShadowserver honeypots (KEV)
Vectorlocal
Privilegesauthenticated
Known exploitationUnconfirmed
Public exploitUnconfirmed

Weakness, pattern, technique

CWE-921Storage of Sensitive Data in a Mechanism without Access Control
CWE-922Insecure Storage of Sensitive Information
T1005Data from Local System

Public exploit references

No public exploit references are available in this record.

Labels summarize the accepted research assessment. They do not indicate a test against your environment.

Technologies

Your stack

See the directory against your own environment.

Your stack

Check the software in your environment

Book a demo to see how Hinoki identifies affected software and validates exploitability in your environment.

Book a demo